Lung cancer is the leading cause of cancer death and one of the most common cancers among both men and women in the United States. Recent advances in high-resolution imaging set the stage for radiomics to become an active emerging field in cancer research. However, the promise of radiomics is limited by a lack of image standardization tools, because computed tomography (CT) images are often acquired using scanners from different vendors with customized acquisition parameters, posing a fundamental challenge to radiomic studies across sites. To overcome this challenge, especially for large-scale, multi-site radiomic studies, advanced algorithms are required to integrate, standardize, and normalize CT images from multiple sources. We propose to develop STAN-CT, a deep learning software package that can automatically standardize and normalize a large volume of diagnostic images to facilitate cross-site large-scale image feature extraction for lung cancer characterization and stratification. STAN-CT will enable a wide range of radiomic researches to identify diagnostic image features that strongly associated with lung cancer prognosis.
https://fei-lab.org/wp-content/uploads/2018/04/empty-300x138.png 0 0 awp-admin https://fei-lab.org/wp-content/uploads/2018/04/empty-300x138.png awp-admin2019-07-01 12:44:522023-12-28 20:01:11New NIH Grant to Support Research on Deep Learning for CT Image Standardization